Dataconomy

AUGUST 30, 2017

Rich Miner — Android co-founder, formerly Google Ventures, now Google — asked me recently “What if companies managed their data like they manage their money?” It’s a basic but profound question that merits some thoughts based on my 25 years managing both information and financial functions in technology and data. The post What if companies managed their data as carefully as they manage their money?

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Eugene Yan

AUGUST 12, 2017

OMSCS CS6300 (Software Development Process) - Java and collaboratively developing an Android app.

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Ines Montani

AUGUST 4, 2017

I’m excited and proud to finally share what we’ve been working on since launching Explosion AI , alongside our NLP library spaCy and our consulting projects. Prodigy is a project very dear to my heart and seeing it come to life has been one of the most exciting experiences as a software developer so far. A lot of the consulting projects we’ve worked on in the past year ended up circling back to the problem of labelling data to train custom models.

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Explosion

AUGUST 3, 2017

Machine learning systems are built from both code and data. It’s easy to reuse the code but hard to reuse the data, so building AI mostly means doing annotation. This is good, because the examples are how you program the behaviour – the learner itself is really just a compiler. What’s not good is the current technology for creating the examples. That’s why we’re pleased to introduce Prodigy , a downloadable tool for radically efficient machine teaching.

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Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

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Christian Haschek

AUGUST 3, 2017

If you have invested in crypto currencies in order to hodl them you might find yourself checking t

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Depends on the Definition

AUGUST 25, 2017

In this post, I will introduce you to something called Named Entity Recognition (NER). NER is a part of natural language processing (NLP) and information retrieval (IR). The task in NER is to find the entity-type of words.

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Eugene Yan

AUGUST 25, 2017

What is data science, how to pick it up, and how to enter the field? A discussion with SMU undergrads.

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Dataconomy

AUGUST 28, 2017

Technologies in the field of data science are progressing at an exponential rate. The introduction of Machine Learning has revolutionized the world of data science by enabling computers to classify and comprehend large data sets. Another important innovation which has changed the paradigm of the world of the tech world. The post Trends Shaping Machine Learning in 2017 appeared first on Dataconomy.

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Dataconomy

AUGUST 11, 2017

In 2016, Google’s net worth was reported to be $336 billion, and this is largely due to the advanced learning algorithms the company employs. Google was the first company to realize the importance of incorporating machine learning in business processes. And the technology powerhouse doesn’t stop at any given point; it keeps. The post Why Businesses Should Embrace Machine Learning appeared first on Dataconomy.

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Dataconomy

AUGUST 14, 2017

Just a few years ago, enterprise organizations had to be convinced that Big Data was a real-world opportunity worth investing in. By 2016, 63% of those enterprise leaders were saying they considered Big Data and advanced analytics initiatives a necessity in order to remain competitive. This year started with even. The post Steering Big Data Projects in the Modern Enterprise appeared first on Dataconomy.

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Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

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Dataconomy

AUGUST 16, 2017

Google regularly gets voted as the best company to work for in USA – its employees get generous paid holidays, free food and are even encouraged to take power naps during the work day in those ‘nap pods’. Google has been providing an excellent workplace atmosphere to its staff – The post Improving Employee Management using Big Data appeared first on Dataconomy.

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Dataconomy

AUGUST 18, 2017

The rise of financial technology and digital payment solutions is helping the world go cashless. Cashless payment methods now cover a wide range of technologies – there are physical cards, online gateways, mobile apps, and digital wallets. Blockchain-enabled payments and cryptocurrencies are also on the rise. Methods are enjoying varying. The post How blockchain is changing the way we pay appeared first on Dataconomy.

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Dataconomy

AUGUST 4, 2017

Big data and sports analytics are changing the ways many things in sports have traditionally been done. They are allowing for new processes that have the potential to alter the way that organizations conduct their scouting. This is because data science and sports analytics are opening up new data points. The post Big Data is changing the future of NBA scouting appeared first on Dataconomy.

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Dataconomy

AUGUST 14, 2017

Artificial Intelligence, Machine Learning, GDPR, Building up a Data Science Team: Trends that impact every modern organization. What is the best way to deal with these challenges and become a successful data driven organization? For the third consecutive year, Big Data Expo and GoDataDriven conduct Big Data Survey: the international benchmark. The post What makes organizations successful with data?

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Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

Dataconomy

AUGUST 23, 2017

It’s not new news that Europe is suffering from a near-chronic skills gap. It’s been going on for a while now, with industry experts and government bodies all scratching their heads over how to solve it. The problem is about to get a whole lot worse, as the soon-to-be-enforced General. The post GDPR and the skills gap that could cost you €20million appeared first on Dataconomy.

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Dataconomy

AUGUST 21, 2017

Many marketers representing mid-market financial services companies labor under the impression that due to their size and scope, the data-driven marketing tactics used by the dominant players are simply out of reach for them. This is a shame and quite far from the truth. Data, analytics, technology and the overall. The post 5 Misconceptions About Data-Driven Financial Services Marketing appeared first on Dataconomy.

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Dataconomy

AUGUST 2, 2017

One of the great things about running a startup is that you’re working with a clean slate. If you have worked with a different organization before, you may have had issues with culture and people that you probably don’t want happening with your own company. Existing organizations, especially the established. The post How to promote a culture of data stewardship for your startup appeared first on Dataconomy.

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Dataconomy

AUGUST 11, 2017

Linear regression is a basic tool. It works on the assumption that there exists a linear relationship between the dependent and independent variable, also known as the explanatory variables and output. However, not all problems have such a linear relationship. In fact, many of the problems we see today are. The post Performing Nonlinear Least Square and Nonlinear Regressions in R appeared first on Dataconomy.

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Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

Data Science

Dataconomy

AUGUST 25, 2017

A recent Cowen survey reveals that businesses are showing increased adoption of cloud computing. Leaders Amazon Web Services (AWS) and Microsoft Azure also continue to control majority of the public cloud market. Organizations are also looking to benefit from increased cloud adoption. Design software giant Adobe’s Q2 earnings report showed 27 percent growth.

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Dataconomy

AUGUST 29, 2017

Franz Inc., an early innovator in Artificial Intelligence (AI) and leading supplier of Semantic Graph Database technology for Knowledge Graphs, recently announced Gruff v7.0, the industry’s leading Graph Visualization software for exploring and discovering connections within data. Gruff provides novice users and graph experts the ability to visually build queries and explore.

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Explosion

AUGUST 4, 2017

Prodigy is a project very dear to my heart and seeing it come to life has been one of the most exciting experiences as a software developer so far.

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Explosion

AUGUST 22, 2017

Sometimes you want to fine-tune a pre-trained model to add a new label or correct some specific errors. This can introduce the "catastrophic forgetting" problem. Pseudo-rehearsal is a good solution: use the original model to label examples, and mix them through your fine-tuning updates.

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Speaker: Kevin Kai Wong, President of Emergent Energy Solutions

In today's industrial landscape, the pursuit of sustainable energy optimization and decarbonization has become paramount. Manufacturing corporations across the U.S. are facing the urgent need to align with decarbonization goals while enhancing efficiency and productivity. Unfortunately, the lack of comprehensive energy data poses a significant challenge for manufacturing managers striving to meet their targets.

Analytics

Depends on the Definition

AUGUST 11, 2017

In this article, we will apply the concept of multi-label multi-class classification with neural networks from the last post, to classify movie posters by genre. First we import the usual suspects in python. import numpy as np import pandas as pd import glob import scipy.

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Depends on the Definition

AUGUST 10, 2017

Often in machine learning tasks, you have multiple possible labels for one sample that are not mutually exclusive. This is called a multi-class, multi-label classification problem. Obvious suspects are image classification and text classification, where a document can have multiple topics.

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Explosion

AUGUST 3, 2017

Machine learning systems are built from both code and data. It's easy to reuse the code but hard to reuse the data, so building AI mostly means doing annotation. This is good, because the examples are how you program the behaviour – the learner itself is really just a compiler. What's not good is the current technology for creating the examples. That's why we're pleased to introduce Prodigy, a downloadable tool for radically efficient machine teaching.

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Christian Haschek

AUGUST 16, 2017

We have been busy the last year in polishing up an internal tool to a finished product.

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Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

AI

Explosion

AUGUST 22, 2017

Sometimes you want to fine-tune a pre-trained model to add a new label or correct some specific errors. This can introduce the “catastrophic forgetting” problem. Pseudo-rehearsal is a good solution: use the original model to label examples, and mix them through your fine-tuning updates. The catastrophic forgetting problem occurs when you optimise two learning problems in succession, with the weights from the first problem used as part of the initialisation for the weights of the second problem.

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